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Abstract

It is difficult for a single-feature tracking algorithm to achieve strong robustness under a complex environment. To solve this problem, we proposed a multifeature fusion tracking algorithm that is based on game theory. By focusing on color and texture features as two gamers, this algorithm accomplishes tracking by using a mean shift iterative formula to search for the Nash equilibrium of the game. The contribution of different features is always keeping the state of optical balance, so that the algorithm can fully take advantage of feature fusion. According to the experiment results, this algorithm proves to possess good performance, especially under the condition of scene variation, target occlusion, and similar interference.

References

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a The values before the brackets are the success rate (%) in threshold=0.5. The values in the brackets are the average CLE (pixels). The optimal results of each sequence are red; the suboptimal results of each sequence are green. The “−” indicates the average CLE of the corresponding algorithm is out of the range of the computing platform.

a The values before the brackets are the success rate (%) in threshold=0.5. The values in the brackets are the average CLE (pixels). The optimal results of each sequence are red; the suboptimal results of each sequence are green. The “−” indicates the average CLE of the corresponding algorithm is out of the range of the computing platform.